Title: Noise reduction algorithm of corrosion acoustic emission signal based on short-time fractal dimension enhancement 

Author: YU Yang; ZHANG Wenwen; YANG Ping;  

Affiliation: Shenyang University of Technology, School of Information Science and Engineering et al.  

Abstract: The general corrosion and local corrosion of Q235 steel were tested by acoustic emission(AE) detecting system under 6%FeCl_3-6H_O solution to effectively detect the corrosion acoustic emission signal from complex background noise.  The short-time fractal dimension and discrete fractional cosine transform methods are combined to reduce noise.  The input SNR is 0~15 dB while corrosion acoustic emission signals being added with white noise, color noise and pink noise respectively.  The results show that the output signal-to-noise ratio is improved by up to 8 dB compared with discrete cosine transform and discrete fractional cosine transform.  The above-mentioned noise reduction method is of significance for the identification of corrosion induced acoustic emission signals and the evaluation of the metal remaining life.